Temporal Planning Research Articles

Decidability and Complexity of Action-Based Temporal Planning over Dense Time

This paper studies the computational complexity of temporal planning, as represented by PDDL 2.1, interpreted over dense time. When time is considered discrete, the problem is known to be EXPSPACE-complete. However, the official PDDL 2.1 semantics, and many implementations, interpret time as a dense domain. This work provides several results about the complexity of the problem, studying a few interesting cases: whether a minimum amount ϵ of separation between mutually exclusive events is given, in contrast to the separation being simply required to be non-zero, and whether or not actions are allowed to overlap already running instances of themselves. We prove the problem to be PSPACE-complete when self-overlap is forbidden, whereas, when allowed, it becomes EXPSPACE-complete with ϵ-separation and undecidable with non-zero separation. These results clarify the computational consequences of different choices in the definition of the PDDL 2.1 semantics, which were vague until now.

Instantaneous reaction-time in dynamic consistency checking of conditional simple temporal networks

Conditional Simple Temporal Networks (CSTNs) is a constraint based graph-formalism for conditional temporal planning. Three notions of consistency arise for CSTNs: weak, strong, and dynamic. Dynamic-Consistency (DC) is the most interesting notion, but it is also the most challenging. In order to address the DC-Checking problem, Comin and Rizzi [12] introduced ε-DC (a refined, more realistic, notion of DC), providing an algorithmic solution to it. Next, given that DC implies ε-DC for some sufficiently small ε>0, and that for every ε>0 it holds that ε-DC implies DC, it was offered a sharp lower bounding analysis on the critical value of the reaction-timeεˆ under which the two notions coincide. This delivered the first (pseudo) singly-exponential time algorithm for the DC-Checking of CSTNs. However, the ε-DC notion is interesting per se, and the ε-DC-Checking algorithm of Comin and Rizzi [12] rests on the assumption that the reaction-time satisfies ε>0; leaving unsolved the question of what happens when ε=0. In this work, we introduce and study π-DC, a sound notion of DC with an instantaneous reaction-time (i.e., one in which the planner can react to any observation at the same instant of time in which the observation is made). Firstly, we demonstrate by a counter-example that π-DC is not equivalent to 0-DC, and that 0-DC is actually inadequate for modeling DC with an instantaneous reaction-time. This shows that the main results obtained in our previous work do not apply directly, as they were formulated, to the case of ε=0. Motivated by this observation, as a second contribution, our previous tools are extended in order to handle π-DC, and the notion of ps-tree is introduced, also pointing out a relationship between π-DC and consistency of Hyper Temporal Networks. Thirdly, a simple reduction from π-DC to (classical) DC is identified. This allows us to design and to analyze the first sound-and-complete π-DC-Checking algorithm. Remarkably, the time complexity of the proposed algorithm remains (pseudo) singly-exponential in the number of propositional letters. Finally, it is observed that the technique can be leveraged to actually reduce from π-DC to 1-DC, this allows us to further improve the exponents in the time complexity.

Planning for Hybrid Systems via Satisfiability Modulo Theories

Planning for hybrid systems is important for dealing with real-world applications, and PDDL+ supports this representation of domains with mixed discrete and continuous dynamics. In this paper we present a new approach for planning for hybrid systems, based on encoding the planning problem as a Satisfiability Modulo Theories (SMT) formula. This is the first SMT encoding that can handle the whole set of PDDL+ features (including processes and events), and is implemented in the planner SMTPlan. SMTPlan not only covers the full semantics of PDDL+, but can also deal with non-linear polynomial continuous change without discretization. This allows it to generate plans with non-linear dynamics that are correct-by-construction. The encoding is based on the notion of happenings, and can be applied on domains with nonlinear continuous change. We describe the encoding in detail and provide in-depth examples. We apply this encoding in an iterative deepening planning algorithm. Experimental results show that the approach dramatically outperforms existing work in finding plans for PDDL+ problems. We also present experiments which explore the performance of the proposed approach on temporal planning problems, showing that the scalability of the approach is limited by the size of the discrete search space. We further extend the encoding to include planning with control parameters. The extended encoding allows the definition of actions to include infinite domain parameters, called control parameters. We present experiments on a set of problems with control parameters to demonstrate the positive effect they provide to the approach of planning via SMT.

Оптимизация предоперационной верификации изолированных аномалий среднего уха при помощи виртуальной КТ-эндоскопии

Isolated anomalies of the middle ear are one of the most difficult areas of practical otorhinolaryngology. Its relevance is due to the rare occurrence, high technical complexity of performing surgical treatment, and the high frequency of unsatisfactory results of surgical interventions. One of the most important factors determining the success of medical actions is high-quality X-ray imaging at the diagnostic stage. It allows you to correctly assess the anatomical features of the temporal bone and plan the tactics of the surgeon. Over the past few years, new technological possibilities have been introduced for the imaging of the ear. Over the past several decades, multispiral computed tomography has been the gold standard of radiation research used in otosurgery. This study has established itself as an informative method for diagnosing middle ear pathology and should be used before planning surgical interventions, as well as a monitoring tool in the postoperative period and for assessing the course of middle ear diseases. With the development of computer-mathematical technologies, over time, it became possible to model the result in three-dimensional space on the basis of axial projection, which led to the emergence of a branch of radiation diagnostics, called virtual CT endoscopy. The work evaluates the specificity of virtual CT endoscopy performed at the preoperative stage in patients with isolated anomalies of the ossicle chain with intact stapes, in terms of diagnosing the continuity of the ossicle chain, changes in their configuration and adhesions with the surrounding bone structures. A comparative analysis of the specificity of standard two-dimensional computed tomography and virtual CT endoscopy was performed, where explorative tympanotomy was used as test results in 43 patients. The advantages of virtual CT endoscopy in visualization of minor defects in the structures of the tympanic cavity were revealed. The importance of virtual CT endoscopy in measuring the tympanic cavity in arbitrary planes was determined. In addition to the above, virtual endoscopy opens up new possibilities for measuring the anatomical structures of the middle ear in arbitrary planes, which makes it possible, for example, to calculate the length of the prosthesis intended for use as plasty of the ossicular chain before surgery.

FUNCTIONS OF URBAN PUBLIC SPACES

The following article proposes a model of urban public spaces functionality and functions evaluation, based on eight basic and two additional functions, which include the following ones: communicational, transitional, activities implementation, different needs satisfaction, aesthetic, recreational, educational, symbolic, temporal and narrative functions. Developing such model may provide better understanding of urban physiology and probably set the first point for new types of planning in future, based on spaces functionality, such as temporal city planning.

A new management framework for western Mediterranean demersal fisheries

The Common Fisheries Policy in the Mediterranean has been so far based on technical measures that have been relatively stable for a long time, and it did not prevent the efficiency increase in both vessels and gears that have counterbalanced the fleet reduction. The new Multiannual Plan for Demersal fish stocks in the western Mediterranean Sea introduces a fishing effort regime as a new approach to reduce significantly fishing time, allowing stocks to approach MSY in the medium term. However, different approaches to reduce fishing time may have different socio-economic impacts that have to be considered. The reduction of fishing time has to be complemented with selectivity improvements, temporal and permanent closures and local co-management plans to protect both juveniles and spawners. The combination of several measures will soften the need for effort reduction and it will contribute significantly to the sustainability of Mediterranean Fisheries.

INVESTIGATING TEMPORAL AND SPATIAL EFFECTS OF URBAN PLANNING VARIABLES ON CRIME RATE: A GWR AND OLS BASED APPROACH

Abstract. Spatial, temporal, environmental and urban planning variables are some factors effected crime occurrence as a social undesirable phenomenon. In this paper, the effect of urban planning variables (including land use diversity and police station area) and temporal parameters (including daily and weekly time windows) on crime incidence were investigated by doing some spatial-temporal analysis. To tackle this, at first, in order to determine crime clusters, DBSCAN algorithm is utilized. Then by using the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) the global effect of urban planning variables on crime spatial clusters are modelled and compared. Finally, the OLS method is used to investigate the effects of temporal variables on crime spatial clusters. The results demonstrate that there is a negative correlation between crime clusters and the police station coverage, and a positive relationship between crime clusters and land-use diversity. Furthermore, increasing the number of police stations in districts with high land-use diversity can result in reducing the crime rate. Also, the result of our temporal analysis shows that the high crime rate was happened at noon and on weekdays. Therefore, increasing the number of police officers at noon and on weekdays in the districts with high land-use diversity is recommended.

Heuristic search via graphical structure in temporal interval-based planning for deep space exploration

Operations of conventional spacecraft used to be planned on ground and are uploaded as telecommands and executed on board at due time. However, because of difficulties in communicating with distant spacecraft, direct human control for the spacecraft is infeasible. Therefore, great hopes are placed on automated planning techniques to enhance the security of the spacecraft. By deciding a coon, as to support opportunistic science, the planner is mplex set of activities or states, an onboard planner is able to effectively arrange the daily tasks on a spacecraft. In additialso required to respond in the shortest possible time. Typically, to better characterize the spacecraft, the timeline-based knowledge representation benefits from its powerful ability to describe time and temporal behaviors, which is essential to effectively address real world problems. Compliant with the representation method, an elegant approach is devised for search guidance and solving problems efficiently in space-like contexts. Specifically, the key technique we build on is the heuristic estimate strategy based on a graphical structure defined in the model. Furthermore, a search algorithm joint with the heuristic function is proposed to avoid redundant work. By evaluating the branching nodes, this approach is able to prune irrelevant search space and make improvements in onboard planning efficiency. Our experiments exhibit an excellent performance on tested instances compared to Europa2.

A DDS-Based Wait-Free Phase-Continuous Carrier Frequency Modulation Strategy for EMI Reduction in FPGA-Based Motor Drive

In ac motor drives, the fixed-frequency harmonic components of output voltage and current from the inverter with fixed-frequency pulsewidth modulation usually lead to electromagnetic interference (EMI). The spread spectrum clock generation (SSCG) is a widely used solution for this problem. Adjusting the switching frequency to reduce EMI is one kind of practicable scheme among SSCG methods. How to design an optimal or suitable modulation profile is a research emphasis of scholars and has been discussed in depth in many literatures. However, apart from the modulation profile, the mode and quality of carrier are also important and can be improved. In most frequency modulation methods, due to the limitation of the conventional carrier generation mode, the implementation of the new frequency instruction has to wait for the termination of the last switching period. In order to eliminate the waiting state and design a simpler algorithm, this paper has proposed a wait-free phase-continuous carrier frequency modulation (WPCFM) strategy by combining the direct digital frequency synthesizer theory and proper temporal planning of control interruptions. Besides, a theoretical analysis of WPCFM, including quantization error, frequency jitter, phase delay, and voltage distortion, has been finished. Moreover, compared with conventional methods, a more convenient, feasible, and simpler field-programmable gate array based algorithm implementation method and the control structure of WPCFM are also introduced. The analysis shows that, although WPCFM causes a slight increase of the current ripple, it can solve the partial frequency nonuniform distribution problem of the conventional method, and it has a potential value of applications in the wide band gap motor drive systems. The effectiveness of the WPCFM is verified by several sets of EMI reduction experiments where classical periodic carrier frequency modulations are applied.

Temporal Planning with Temporal Metric Trajectory Constraints

In several industrial applications of planning, complex temporal metric trajectory constraints are needed to adequately model the problem at hand. For example, in production plants, items must be processed following a “recipe” of steps subject to precise timing constraints. Modeling such domains is very challenging in existing action-based languages due to the lack of sufficiently expressive trajectory constraints.We propose a novel temporal planning formalism allowing quantified temporal constraints over execution timing of action instances. We build on top of instantaneous actions borrowed from classical planning and add expressive temporal constructs. The paper details the semantics of our new formalism and presents a solving technique grounded in classical, heuristic forward search planning. Our experiments prove the proposed framework superior to alternative state-of-theart planning approaches on industrial benchmarks, and competitive with similar solving methods on well known benchmarks took from the planning competition.

Efficient Temporal Planning Using Metastates

When performing temporal planning as forward state-space search, effective state memoisation is challenging. Whereas in classical planning, two states are equal if they have the same facts and variable values, in temporal planning this is not the case: as the plans that led to the two states are subject to temporal constraints, one might be extendable into at temporally valid plan, while the other might not. In this paper, we present an approach for reducing the state space explosion that arises due to having to keep many copies of the same ‘classically’ equal state – states that are classically equal are aggregated into metastates, and these are separated lazily only in the case of temporal inconsistency. Our evaluation shows that this approach, implemented in OPTIC and compared to existing state-of-the-art memoisation techniques, improves performance across a range of temporal domains.

Congestion Graphs for Automated Time Predictions

Time prediction is an essential component of decision making in various Artificial Intelligence application areas, including transportation systems, healthcare, and manufacturing. Predictions are required for efficient resource allocation and scheduling, optimized routing, and temporal action planning. In this work, we focus on time prediction in congested systems, where entities share scarce resources. To achieve accurate and explainable time prediction in this setting, features describing system congestion (e.g., workload and resource availability), must be considered. These features are typically gathered using process knowledge, (i.e., insights on the interplay of a system’s entities). Such knowledge is expensive to gather and may be completely unavailable. In order to automatically extract such features from data without prior process knowledge, we propose the model of congestion graphs, which are grounded in queueing theory. We show how congestion graphs are mined from raw event data using queueing theory based assumptions on the information contained in these logs. We evaluate our approach on two real-world datasets from healthcare systems where scarce resources prevail: an emergency department and an outpatient cancer clinic. Our experimental results show that using automatic generation of congestion features, we get an up to 23% improvement in terms of relative error in time prediction, compared to common baseline methods. We also detail how congestion graphs can be used to explain delays in the system.

Robustness Envelopes for Temporal Plans

To achieve practical execution, planners must produce temporal plans with some degree of run-time adaptability. Such plans can be expressed as Simple Temporal Networks (STN), that constrain the timing of action activations, and implicitly represent the space of choices for the plan executor.A first problem is to verify that all the executor choices allowed by the STN plan will be successful, i.e. the plan is valid. An even more important problem is to assess the effect of discrepancies between the model used for planning and the execution environment.We propose an approach to compute the “robustness envelope” (i.e., alternative action durations or resource consumption rates) of a given STN plan, for which the plan remains valid. Plans can have boolean and numeric variables as well as discrete and continuous change. We leverage Satisfiability Modulo Theories (SMT) to make the approach formal and practical.

Automated Verification of Social Laws for Continuous Time Multi-Robot Systems

Designing multi-agent systems, where several agents work in a shared environment, requires coordinating between the agents so they do not interfere with each other. One of the canonical approaches to coordinating agents is enacting a social law, which applies restrictions on agents’ available actions. A good social law prevents the agents from interfering with each other, while still allowing all of them to achieve their goals. Recent work took the first step towards reasoning about social laws using automated planning and showed how to verify if a given social law is robust, that is, allows all agents to achieve their goals regardless of what the other agents do. This work relied on a classical planning formalism, which assumed actions are instantaneous and some external scheduler chooses which agent acts next. However, this work is not directly applicable to multi-robot systems, because in the real world actions take time and the agents can act concurrently. In this paper, we show how the robustness of a social law in a continuous time setting can be verified through compilation to temporal planning. We demonstrate our work both theoretically and on real robots.

Temporal Planning as Refinement-Based Model Checking

Planning as model checking based on source-to-source compilations has found increasing attention. Previously proposed approaches for temporal and hybrid planning are based on static translations, in the sense that the resulting model checking problems are uniquely defined by the given input planning problems. As a drawback, the translations can become too large to be efficiently solvable. In this paper, we address propositional temporal planning, lifting static translations to a more flexible framework. Our framework is based on a refinement cycle that allows for adaptively computing suitable translations of increasing size. Our experiments on temporal IPC domains show that the resulting translations to timed automata often become succinct, resulting in promising performance when applied with the directed model checker MCTA.

Replanning for Situated Robots

Planning enables intelligent agents, such as robots, to act so as to achieve their long term goals. To make the planning process tractable, a relatively low fidelity model of the world is often used, which sometimes leads to the need to replan. The typical view of replanning is that the robot is given the current state, the goal, and possibly some data from the previous planning process. However, for robots (or teams of robots) that exist in continuous physical space, act concurrently, have deadlines, or must otherwise consider durative actions, things are not so simple. In this paper, we address the problem of replanning for situated robots. Relying on previous work on situated temporal planning, we frame the replanning problem as a situated temporal planning problem, where currently executing actions are handled via Timed Initial Literals (TILs), under the assumption that actions cannot be interrupted. We then relax this assumption, and address situated replanning with interruptible actions. We bridge the gap between the low-level model of the robot and the high-level model used for planning by the novel notion of a bail out action generator, which relies on the low-level model to generate highlevel actions that describe possible ways to interrupt currently executing actions. Because actions can be interrupted at different times during their execution, we also propose a novel algorithm to handle temporal planning with time-dependent durations.

DREAM: An Algorithm for Mitigating the Overhead of Robust Rescheduling

Generating and executing temporal plans is difficult in uncertain environments. The current state-of-the-art algorithm for probabilistic temporal networks maintains a high success rate by rescheduling frequently as uncertain events are resolved, but this approach involves substantial resource overhead due to computing and communicating new schedules between agents. Aggressive rescheduling could thus reduce overall mission duration or success in situations where agents have limited energy or computing power, and may not be feasible when communication is limited. In this paper, we propose new approaches for heuristically deciding when rescheduling is most efficacious. We propose two compatible approaches, Allowable Risk and Sufficient Change, that can be employed in combination to compromise between the computation rate, the communication rate, and success rate for new schedules. We show empirically that both approaches allow us to gracefully trade success rate for lower computation and/or communication as compared to the state-of-the-art dynamic scheduling algorithm.

Risk-based Operational Planning and Scheduling Model for an Emergency Medical Center

In order to deal with high uncertainty and variability in emergency medical centers, many researchers have developed various models for their operational planning and scheduling. However, most of the models just provide static plans without any risk measures as their results, and thus the users often lose the opportunity to analyze how much risk the patients have, whether the plan is still implementable or how the plan should be changed when an unexpected event happens. In this study, we construct a simulation model combined with a risk-based planning and scheduling module designed by Simio LLC. In addition to static schedules, it provides possibility of treatment delay for each patient as a risk measure, and updates the schedule to avoid the risk when it is needed. By using the simulation model, the users can experiment various scenarios in operations quickly, and also can make a decision not based on their past experience or intuition but based on scientific estimation of risks even in urgent situations. An example of such an operational decision making process is demonstrated for a real mid-size emergency medical center located in Seoul, Republic of Korea. The model is designed for temporal short-term planning especially, but it can be expanded for long-term planning also with some appropriate adjustments.

Autonomous Driving Motion Planning With Constrained Iterative LQR

Motion planning is a core technique for autonomous driving. Nowadays, there still exists a lot of challenges in motion planning for autonomous driving in complicated environments due to: 1) the need of both spatial and temporal planning in highly dynamic environments; 2) nonlinear vehicle dynamic models and non-convex collision avoidance constraints; and 3) the need of high computation efficiency for real-time implementation. Iterative linear quadratic regulator (ILQR) is an algorithm to solve the optimal control problem with nonlinear system very efficiently. However, it can not deal with constraints. In this paper, the constrained iterative LQR (CILQR) is proposed to efficiently solve the optimal control problem with nonlinear system dynamics and general form of constraints. An autonomous driving motion planning problem is then formulated and solved using CILQR. Simulation case studies show the capability of the CILQR algorithm to solve these kind of problems and the computation efficiency of CILQR is shown to be much higher than the standard SQP solver.

On-board Automated Mission Planning for Spacecraft Autonomy: A Survey

Traditionally, space mission operations are based on preplanned activities fulfilled by the ground segment. Due to restricted visibility windows and communication delays, some space systems cannot be completely controlled from the ground in real-time. A way to overcome this issue is to equip the satellite flight software with an autonomous decision-making system such as onboard automated planning techniques. This paper presents a comprehensive survey on goal-based mission planning to increase the level of spacecraft autonomy. The most used onboard planning techniques for space missions such as temporal and hierarchical planning are presented in details. We describe several onboard planners and compare them by design choices and performance results. Research trends in goal-based planning for the space domain are also outlined. This paper highlights the automated mission planning, whose state of the art has not been deeply explored by other surveys on autonomous spacecraft.